# Search Results: 'Convex Optimization'

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8 results for "Convex Optimization"
Convex Optimization & Euclidean Distance Geometry
Paperback: \$100.00
Optimization is the science of making a best choice in the face of conflicting requirements. Any convex optimization problem has geometric interpretation. If a given optimization problem can be... More > transformed to a convex equivalent, then this interpretive benefit is acquired. That is a powerful attraction: the ability to visualize geometry of an optimization problem. Conversely, recent advances in geometry hold convex optimization within their proofs' core. This book is about convex optimization, convex geometry (with particular attention to distance geometry), geometrical problems, and problems that can be transformed into geometrical problems. Euclidean distance geometry is, fundamentally, a determination of point conformation from interpoint distance information; e.g., given only distance information, determine whether there corresponds a realizable configuration of points; a list of points in some dimension that attains the given interpoint distances. 2005 International Edition I< Less
Convex Optimization & Euclidean Distance Geometry
Paperback: \$99.99
Convex Analysis is the calculus of inequalities while Convex Optimization is its application. Analysis is inherently the domain of the mathematician while Optimization belongs to the engineer. In... More > layman’s terms, the mathematical science of Optimization is the study of how to make a good choice when confronted with conflicting requirements. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any Convex Optimization problem has geometric interpretation, this book is about convex geometry (with particular attention to distance geometry), and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convex problems. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. Revised & Enlarged International Paperback Edition III< Less
Machine Learning (Convex Optimization for Sissies) By
Paperback: \$23.41
There are many reasons why people are purchasing an item which is not related to basic needs such foods, clothes, and living place. However, those items will make the life complete and just great.... More > Machine Learning is specially designed to satisfy people who need additional items for their life. This item is great because it has undergone many researches to make this product perfect. By purchasing this product, a brand new experience of having something great will be owned. If experience is not what people really seek, they will get better satisfaction upon purchasing this product. Some people may had tried to purchase similar items and came into disappointment. While it was bad to hear, please do not make it as the dead end. This Machine Learning product is different from the others. Not only is it because of years of researches, but also because of great care which is becoming the main concern by the maker of this product.< Less
Convex Optimization Euclidean Distance Geometry 2e By
Paperback: \$41.09
Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is the domain of the mathematician while Optimization belongs to the engineer. In... More > layman’s terms, the mathematical science of Optimization is a study of how to make a good choice when faced with conflicting requirements. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any convex optimization problem has geometric interpretation, this book is about convex geometry (with particular attention to distance geometry) and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convexity. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. This is a BLACK & WHITE paperback. A hardcover with full color interior, as originally conceived, is available at lulu.com/spotlight/dattorro< Less
Convex Optimization Euclidean Distance Geometry 2e By
Hardcover: \$140.92
Convex Analysis is an emerging calculus of inequalities while Convex Optimization is its application. Analysis is the domain of the mathematician while Optimization belongs to the engineer. In... More > layman’s terms, the mathematical science of Optimization is a study of how to make a good choice when faced with conflicting requirements. The qualifier Convex means: when an optimal solution is found, then it is guaranteed to be a best solution; there is no better choice. As any convex optimization problem has geometric interpretation, this book is about convex geometry (with particular attention to distance geometry) and nonconvex, combinatorial, and geometrical problems that can be relaxed or transformed into convexity. A virtual flood of new applications follows by epiphany that many problems, presumed nonconvex, can be so transformed. Full Color Interior Hardcover< Less
Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems
Paperback: \$14.99
Optimization and control problems often need to be formulated in a way that takes the uncertainty of the future into account in order to accurately reflect a "good" decision that can stand... More > up to a variety of possible future outcomes. One way of including uncertainty in such problems treats the uncertain parameters as a random vector with an underlying probability distribution. Doing this creates a stochastic programming model which is inherently infinite dimensional, or at best extremely large, in particular when many time stages are present. In order to solve such problems, a good approximation framework is needed that encompasses various approaches such as sampling and analytical methods for various problem classes. Complementing this should be a development of solution procedures that exploit a problem's structure, for example taking advantage of convexity and decomposability wherever possible. This dissertation addresses these key issues in four parts.< Less
Approximation and Solution Schemes for Stochastic Dynamic Optimization Problems
eBook (PDF): \$6.99
Optimization and control problems often need to be formulated in a way that takes the uncertainty of the future into account in order to accurately reflect a "good" decision that can stand... More > up to a variety of possible future outcomes. One way of including uncertainty in such problems treats the uncertain parameters as a random vector with an underlying probability distribution. Doing this creates a stochastic programming model which is inherently infinite dimensional, or at best extremely large, in particular when many time stages are present. In order to solve such problems, a good approximation framework is needed that encompasses various approaches such as sampling and analytical methods for various problem classes. Complementing this should be a development of solution procedures that exploit a problem's structure, for example taking advantage of convexity and decomposability wherever possible. This dissertation addresses these key issues in four parts.< Less
Learning algorithms and statistical software, with applications to bioinformatics
Paperback: \$50.00
Toby Dylan Hocking's PhD thesis is divided into two parts: learning algorithms and statistical software. Algorithms for segmentation, clustering, and model selection are discussed in the first part,... More > and the second part explains how to implement several statistical software packages in the R language. The content, which includes 47 color figures, should be appreciated by a reader with a background in statistics, data analysis, machine learning, or programming.< Less

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